{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.01-Simple-Line-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Line Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$.\n",
    "Here we will take a first look at creating a simple plot of this type.\n",
    "As with all the following sections, we'll start by setting up the notebook for plotting and  importing the packages we will use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all Matplotlib plots, we start by creating a figure and an axes.\n",
    "In their simplest form, a figure and axes can be created as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wYf3888/q6OgoxIhzItsezp49qzt37mjv3r365JNPFIvF9N133xVq1LzLtotly5apvLxc\nlZWV8nq9qqure+xTrCXZdjE0NKTz58+rp6dHPT09unXrls6dO1eoUQtqtt3Madhramr0008/SVLW\nm5mSyaT6+/v1yiuv5PLt/1Wy7UKSDhw4oPv37+v48ePpr2QsyraHcDisb775RidOnNA777yjxsZG\nbdy4sVCj5l22XZSVlenevXvpHyLG43G9/PLLBZlzLmTbRXFxsRYtWiSfz5f+2+3o6GihRp1T//s3\n19l20/XO06cRCATU19eX/r40EokoFoulb2bav3+/du/eLcdxFAwGtXz58ly+/b9Ktl2sWbNGp0+f\nVm1trcLhsDwej5qamrRhw4YCT517br8n/kvcdtHe3q59+/ZJktauXav169cXcty8ctvF3/9izOfz\nqby8XJs2bSrwxHPj758lPGs3uUEJAIzhBiUAMIawA4AxhB0AjCHsAGAMYQcAYwg7ABhD2AHAGMIO\nAMb8H/Ams7WyTe/nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102e12390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Matplotlib, the *figure* (an instance of the class ``plt.Figure``) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.\n",
    "The *axes* (an instance of the class ``plt.Axes``) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization.\n",
    "Throughout this book, we'll commonly use the variable name ``fig`` to refer to a figure instance, and ``ax`` to refer to an axes instance or group of axes instances.\n",
    "\n",
    "Once we have created an axes, we can use the ``ax.plot`` function to plot some data. Let's start with a simple sinusoid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kkB09Ktvi0e2WLJHRNt7eqpOQXnl6AjExcq+MH+/gMRz50OLFixEUFIRFixah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\n5P4f+jwGTxP/IVYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107527f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "x = np.linspace(0, 10, 1000)\n",
    "ax.plot(x, np.sin(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can use the pylab interface and let the figure and axes be created for us in the background\n",
    "(see [Two Interfaces for the Price of One](04.00-Introduction-To-Matplotlib.ipynb#Two-Interfaces-for-the-Price-of-One) for a discussion of these two interfaces):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kkB09Ktvi0e2WLJHRNt7eqpOQXnl6AjExcq+MH+/gMRz50OLFixEUFIRFixah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\n5P4f+jwGTxP/IVYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107066518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to create a single figure with multiple lines, we can simply call the ``plot`` function multiple times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3b7o6fO18EZPGzkxERwfw8pLHA0qDo7eOolW9VmhWpxltKaIhhQmTvbk9DHUM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\nPYfD4TAON3oOh8NhnP8DK1eUocOflXQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1076d5cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's all there is to plotting simple functions in Matplotlib!\n",
    "We'll now dive into some more details about how to control the appearance of the axes and lines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adjusting the Plot: Line Colors and Styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first adjustment you might wish to make to a plot is to control the line colors and styles.\n",
    "The ``plt.plot()`` function takes additional arguments that can be used to specify these.\n",
    "To adjust the color, you can use the ``color`` keyword, which accepts a string argument representing virtually any imaginable color.\n",
    "The color can be specified in a variety of ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xR5W7Gc6ekZFBmzZt3I4QsEfz4ObM6jnL/QVadVJRiCHXeywDQNu2bcnKynI/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rQk4I5eV+KXgeUEWM/jcDjIz88nMzNT+6NmS5ISYPli+FPzRJBpO0g2LEF36WcDoq/+Amsoo3kR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1077b3a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x - 0), color='blue')        # specify color by name\n",
    "plt.plot(x, np.sin(x - 1), color='g')           # short color code (rgbcmyk)\n",
    "plt.plot(x, np.sin(x - 2), color='0.75')        # Grayscale between 0 and 1\n",
    "plt.plot(x, np.sin(x - 3), color='#FFDD44')     # Hex code (RRGGBB from 00 to FF)\n",
    "plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1\n",
    "plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines.\n",
    "\n",
    "Similarly, the line style can be adjusted using the ``linestyle`` keyword:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JeEHBIdLSptV6zd09RIiIWwosGBcYSbojid1dd1N8spgbZt5Az+M9af3P1sJE/EhBAQcL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\nHE6LXCmlHE6LXCmlHO4PeozvnTG5Su8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1079d9160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x + 0, linestyle='solid')\n",
    "plt.plot(x, x + 1, linestyle='dashed')\n",
    "plt.plot(x, x + 2, linestyle='dashdot')\n",
    "plt.plot(x, x + 3, linestyle='dotted');\n",
    "\n",
    "# For short, you can use the following codes:\n",
    "plt.plot(x, x + 4, linestyle='-')  # solid\n",
    "plt.plot(x, x + 5, linestyle='--') # dashed\n",
    "plt.plot(x, x + 6, linestyle='-.') # dashdot\n",
    "plt.plot(x, x + 7, linestyle=':');  # dotted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you would like to be extremely terse, these ``linestyle`` and ``color`` codes can be combined into a single non-keyword argument to the ``plt.plot()`` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v3pjUvLnQEE+7mobJP05G5+WdoTVokfRKEr566iurD3GAV+REYul0wPbtwJNPqsfDhwMj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1077be9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x + 0, '-g')  # solid green\n",
    "plt.plot(x, x + 1, '--c') # dashed cyan\n",
    "plt.plot(x, x + 2, '-.k') # dashdot black\n",
    "plt.plot(x, x + 3, ':r');  # dotted red"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These single-character color codes reflect the standard abbreviations in the RGB (Red/Green/Blue) and CMYK (Cyan/Magenta/Yellow/blacK) color systems, commonly used for digital color graphics.\n",
    "\n",
    "There are many other keyword arguments that can be used to fine-tune the appearance of the plot; for more details, I'd suggest viewing the docstring of the ``plt.plot()`` function using IPython's help tools (See [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adjusting the Plot: Axes Limits\n",
    "\n",
    "Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control.\n",
    "The most basic way to adjust axis limits is to use the ``plt.xlim()`` and ``plt.ylim()`` methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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F09smGPZq0QJ48klgyRLZScge8+eL7jRvb9lJnGvcOHGF4spY1GuxbRtwyy3An/8sO4nz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107ab9f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "\n",
    "plt.xlim(-1, 11)\n",
    "plt.ylim(-1.5, 1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xuFfAa6/J2FUjPb0fOAB8/LEMVSNyh9BQoEoVYN061Umcx2YDJk4EpkzRzoAE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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1079fa160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "\n",
    "plt.xlim(10, 0)\n",
    "plt.ylim(1.2, -1.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A useful related method is ``plt.axis()`` (note here the potential confusion between *axes* with an *e*, and *axis* with an *i*).\n",
    "The ``plt.axis()`` method allows you to set the ``x`` and ``y`` limits with a single call, by passing a list which specifies ``[xmin, xmax, ymin, ymax]``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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F09smGPZq0QJ48klgyRLZScge8+eL7jRvb9lJnGvcOHGF4spY1GuxbRtwyy3An/8sO4nz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107508b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.axis([-1, 11, -1.5, 1.5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``plt.axis()`` method goes even beyond this, allowing you to do things like automatically tighten the bounds around the current plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tku6Eh0shO3pUtsWjWy1dKqNtfHxUJyGt8vQEYmPlXpkwwc5j2POhJUuWIDg4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\nBk/pWBptAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d1c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.axis('tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It allows even higher-level specifications, such as ensuring an equal aspect ratio so that on your screen, one unit in ``x`` is equal to one unit in ``y``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BTkSkOQY5EZHmGORERJpjkBMRaY5BTkSkuf8HwX56wZ2U5kIAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1076ded30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.axis('equal');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on axis limits and the other capabilities of the ``plt.axis`` method, refer to the ``plt.axis`` docstring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labeling Plots\n",
    "\n",
    "As the last piece of this section, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends.\n",
    "\n",
    "Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+/hw/fpxLly7h5eXFnj17GDFixE2P5efnV+TzW1FSUpLhrkXNmuDtDb//7keL\nFs47rxGvhS4FXYt58yA0FGrVsv71MvP7IjQUdu+uareuwNNF3LKuUM8gvL298fX1pXr16nh6enLo\n0KFihQsJCaF06dKEh4czdepUxo8fz5o1a1i2bBmenp6MHz+e4cOHExERQf/+/alWrVqxziP0yplV\nLd1MxiXdS+age6+VQs1DfvLJJ7lw4QI1a9YEwM3NjbvvvrvIJ3Nzc2PSdfPHr17Go3379rRv377I\nxxXG07MnPP88FPExlXCC06fhxx/VPsjC2Fq1UhNPf/4Z7rjD+ecvVIE4f/48sbGxjs4iLKRNGzh2\nDE6dglq1dKcRV1uzBrp0UfsgC2Nzd4cePdTf2dNPazh/YV5Ut25dfvvtN0dnERZSqpTaQtEIywWI\na0n3krno7GYqVIFISEigQ4cO3H///bm/hCiIzKo2nr/+UnNUunbVnUQUVqdOEB8Pf/7p/HMXqotp\n06ZNjs4hLKhLF3jkEUhNVaOahH5bt6qNgSpW1J1EFJa3N7RtCxs2gLPXSc23QERHRzNq1ChGjx6N\n23UD2qdPn+7QYML8brnln+UC+vTRnUaA7MJoVjndTM4uEPl2MT3w9yLx7dq1IzAwkLvvvpv9+/fT\ntGlTp4QT5ifdTMaRna2eCcnzB/Pp0UO1IDKcPF8u3wLR8O+twZYtW4a/vz9fffUVo0ePZuvWrU4J\nJ8zPCMsFCCUhQS0CV7++7iSiqPz8oF49+OIL5563UA+pc+Y9XLp0ie7du+cujyFEQerWhRo11EM2\noZd0L5mbjtFMhfqkz8zM5K233qJly5bs3r2bDGe3c4SpSTeTMcjwVnPLKRA2m/POWagCMWXKFG67\n7TYeffRRLly4wLRp0xydS1iI7uUCxD97HLdurTuJKK6mTdVzpO+/d945CzXMtU6dOtSpUweAbt26\nOTKPsKC774bz5+HoUfD3153GNa1ereY+eBbqX7wwopw1zlatgjvvdM455WGCcLic5QJk8T595PmD\nNTi7NS4FQjiFdDPpk5Ki9jfu3Fl3ElFSbdvCkSNw5oxzzicFQjhFp06wdy/88YfuJK5n0yY1YTGf\n/beESZQuDQ8+CGvXOud8UiCEU5QrB+3aqck+wrlk9JK1OLM1LgVCOI10MzlfVpa625QCYR1du8L2\n7WrhRUeTAiGcpkcP2LjR+csFuLJvvoHq1dWERWENlSpBUJBaeNHRpEAIp6lZU89yAa5MupesyVlb\n+kqBEE7UaVVsAAAREUlEQVQl3UzOJcNbrSmnQGRnO/Y8UiCEU+lYLsBVHT2q9jNu1Up3EmFv/v6q\nq2nvXseeRwqEcKqmTdWDU2cuF+CqVq6E3r3VREVhPc5ojctbRzjV1csFCMdauVI2arIyKRDCkpz1\ngM2VnTvnznffwd97fgkLatUKfvsNjh1z3DmkQAina9dOdTH99pvuJNa1ebMXDz4IXl66kwhH8fCA\n7t0de7MlBUI4nbOXC3BFGzZ4SfeSC3B0N5MUCKGFdDM5TkoK7N5dGlmZ3/pCQtRujRcvOub4UiCE\nFl27qpmgly/rTmI9GzdCYGA6FSroTiIczdtbrfC6bp1jji8FQmhRuTIEBsKWLbqTWM/KldC58xXd\nMYSThIbCihWOObYUCKFN376Oe2O7qowM9WxHCoTr6NVLtRod0RqXAiG06dNHPWDLzNSdxDo+/xzq\n14eaNR28BoMwjKpVHdcalwIhtLn9drXK6Oef605iHTI5zjWFhkJcnP2PKwVCaNW3r2Pe2K7IZpMC\n4apCQ9WoQHu3xqVACK1ynkM4elVKV7Bvn9q5r2FD3UmEs912G9xxh/1b41IghFYNGkCFCmostyiZ\nnNaDm5vuJEIHR3QzSYEQ2kk3k31I95Jrc0RrXAqE0C7nzkf2iCi+H3+Ec+fgnnt0JxG65LTG9+yx\n3zGlQAjtWrRQD9cOHtSdxLw+/RT69ZO9H1ydvbuZ5O0ktHNzk26mklq+HMLCdKcQuuX8O7JXa1wK\nhDAEKRDFd+wYnDwJwcG6kwjdWrRQs+nt1RqXAiEMoXVrtT/ETz/pTmI+y5errgUPD91JhG5ubvZd\nm0kKhDAEDw81AkfWZio66V4SV7Nna1wKhDCM0FD1sFUU3vHj8PPPapc+IQDuuw/OnFEj20pKCoQw\njA4dVBfTiRO6k5hHXBz07g2lSulOIozCw0O1KJctK/mxpEAIwyhdWnUz2eON7Sqke0nkZcAAWLKk\n5MeRAiEMZeBA+7yxXcGpU3DkCDzwgO4kwmjuv19NnDxypGTHkQIhDKVDB/jlF9WvLvIXFwc9e6qW\nlxBXc3dXLculS0t4HPvEEcI+PD3VKAzpZiqYdC+J/AwcKAVCWJB0MxXs9Gk4cABCQnQnEUZ1771w\n8SIcOlT8Y3jaL07B0tLSeOGFFzh//jw+Pj5MnTqVihUrXvOayZMns2/fPry9vQGIjo7Gx8fHmTGF\nZm3bqg/AH39U22eKGy1dqkYvlSmjO4kwKnd36N9fvVcmTSrmMewbKX+LFy8mICCARYsW0bt3b6Kj\no294zaFDh5g3bx4LFixgwYIFUhxcUM4wvZI2j63sk08gIkJ3CmF0Od1MxV2byakFIiEhgbZt2wLQ\ntm1bvv7662u+b7PZOH78OBMmTCAiIoJPZdaUy7LXMD0rOnpUPcjv2FF3EmF0rVrB5cvw3XfF+3mH\ndTEtX76c+fPnX/O1KlWq5LYIvL29SUlJueb7f/31F5GRkTz00ENkZmYSFRVF06ZNCQgIcFRMYVBt\n2sCFC3D4MDRqpDuNscTGqhaWp1M7iIUZubmpm62lS6FZs6L/vMPeYmFhYYRdN8TiqaeeIjU1FYDU\n1FR8fX2v+X7ZsmWJjIykTJkylClThnvvvZcjR47kWSCSkpIcFd1UkpOTLXstunYtz7x52YwenVLw\ni7H2tchhs8GCBVV5882LJCWl3/R1rnAtCsvVr0WHDqUYNaoiI0f+XuSfdeo9SGBgIDt37qRp06bs\n3LmTli1bXvP9Y8eO8dxzz/HZZ5+RmZlJQkICffv2zfNYfn5+zohseElJSZa9FiNGwEMPwVtvlS/U\nPstWvhY5DhyAK1egZ88q+W4O5ArXorBc/VrUrKme65054wecLtLPOvUZREREBD/++CODBg1i2bJl\nPPnkkwDExMSwfft2/P396dOnD/379ycqKorQ0FD8/f2dGVEYyD33QHo6fPut7iTGsXgxhIfLznGi\n8NzcYNAgNbChyD9rs5lvJ+CEhASCgoJ0xzAEq98dTZwIly7Bv/9d8Gutfi1sNrjjDrUk+l135f9a\nq1+LopBroZ7ldewIq1cX7bNT7kOEoQ0Zou6aMzN1J9Fv927w8oLmzXUnEWbTqBHUqFH0n5MCIQyt\nfn2oUwe2bNGdRL/Fi9Xch8I8jxHiegsWFP1npEAIwxsyBD7+WHcKvdLT1fDWQYN0JxFm1aRJ0X9G\nCoQwvIEDYc0aSCncaFdLWr8eGjSAevV0JxGuRAqEMLyqVSE42H777JpRTAwMG6Y7hXA1UiCEKURG\num4309mzsH27WnhNCGeSAiFMoWdP2LsXXHFC7OLF0KMHlC+vO4lwNVIghCmULQv9+hVvJIbZSfeS\n0EUKhDCNESPgv/8t/tLFZnTggOpi6tBBdxLhiqRACNO45x61//IXX+hO4jzz50NUlFpLRwhnkwIh\nTMPNTbUi5s7VncQ5MjJg0SJVIITQQQqEMJXISFi1Cv78U3cSx1uzRs17aNBAdxLhqqRACFOpUgUe\nfFCN7LG6Dz6Axx/XnUK4MikQwnRGjIB583SncKyff4Z9+9TOcULoIgVCmE6nTmpkz/79upM4zpw5\nqjvNy0t3EuHKpEAI0/HwgEcegeho3UkcIz0dPvoIHntMdxLh6qRACFN65BFYtgz++EN3EvtbuVKt\n3y8Pp4VuUiCEKVWvDt27qzttq5k9Wx5OC2OQAiFM68knVTdTdrbuJPZz5AgcPAihobqTCCEFQpjY\nPfdAhQqwcaPuJPbz3nuq9VC6tO4kQkiBECbm5gZPPAHvv687iX1cuKDmd4wcqTuJEIoUCGFq4eEQ\nHw8//aQ7ScnNmQO9ehVvc3khHEEKhDC1smXViKZ339WdpGQyMlRL6NlndScR4h9SIITpPf00fPIJ\nnD9v3rdzXBz4+0OLFrqTCPEP8/6LEuJvNWqoJSliYrx1RykWmw3eeUdaD8J4pEAISxgzBubPL0dq\nqu4kRbd1KyQnq+cPQhiJFAhhCQ0aQKtW6aacOPf66zB+PLjLv0ZhMPKWFJYxalQK06erB75msWsX\nnDgBERG6kwhxIykQwjICAzPw91fbdJrF5Mkwbhx4eupOIsSNpEAIS3ntNfUrLU13koLt2weJiTBs\nmO4kQuRNCoSwlNat4c47zbGh0IQJqvVQpozuJELkTQqEsJxXX4U33oArV3QnubkvvlCL8smeD8LI\npEAIy2nZEoKC1J7ORmSzwYsvqkImrQdhZFIghCW9/jpMmWLMDYVWr4ZLl2DwYN1JhMifFAhhSU2b\nqj0VXn1Vd5JrZWSoOQ9vvKG2ThXCyKRACMt69VVYuBB++EF3kn+8/z7ceiv06KE7iRAFkwIhLKta\nNdXXP2aM7iTKmTNq3sN776m9LIQwOikQwtKeflrtFREXpzsJjB0LI0aoZUGEMAOZvyksrXRptRFP\neDh06AAVK+rJsX07bNsGhw/rOb8QxSEtCGF5wcHQuze88IKe8ycnw/DhMHs2+PrqySBEcUiBEC5h\n6lTYtEn9craxY1XrpXt3559biJKQLibhEsqXh48+gshI+PZbqF7dOeddswbWroUDB5xzPiHsSVoQ\nwmV07KgeEkdGQna248937Jg63+LFUKGC488nhL1JgRAuZeJEtUbThAmOPc+VKzBggBpm26aNY88l\nhKNIgRAuxdMTli+HTz5x3L4R2dmqleLvL/tMC3OTZxDC5VSrpp4LtG8Pfn4QEmK/Y9ts8NxzcPYs\nbNggE+KEuUkLQrikRo3g00/VgnkbNtjnmDmrtG7fDitXgpeXfY4rhC5SIITLuv9+9UEeFQXLlpXs\nWBkZMHKkmgy3fbs8lBbWoKVAbN68mTE3WSBn6dKl9OvXj/DwcHbs2OHcYMLl3HcfbNyoJtGNH68+\n6IvqzBk1QurECdi6FSpXtn9OIXRweoGYPHky//73v/P83rlz51i4cCFLlixh7ty5TJ8+nYzi/IsV\noghatIA9e9T8iLvvhm++KdzPZWWpZTyaNVMFYs0aNd9CCKtweoEIDAzklVdeyfN7Bw4cICgoCE9P\nT3x8fKhTpw4/GGmtZmFZVavC+vVq5dd+/aBLF1ixAlJTb3ztr7/CzJnQsKEaCbV5sxo+6y4dtsJi\nHDaKafny5cy/bhzhlClT6Nq1K/Hx8Xn+TEpKCr5XLVZTrlw5kpOTHRVRiGu4uanhqQMGwKJFMGuW\n+n3t2mq0U1YWHD8OFy9C587w3/+q5xgyUklYlcMKRFhYGGFhYUX6GR8fH1JSUnJ/n5qaSvmbtNkT\nEhJKlM9KTp8+rTuCYdjrWjRvrn4VZN8+u5zOIeR98Q+5FsVjqHkQzZo149133yU9PZ20tDR+/vln\n6tevf8PrgoKCNKQTQgjXYogCERMTQ+3atenQoQORkZEMGjQIm83G6NGjKV26tO54QgjhktxsNptN\ndwghhBDGY6pxFzabjYkTJxIeHk5UVBQnT57UHUmbzMxMxo4dy+DBgxkwYADbtm3THUmr8+fP0759\ne44dO6Y7inYffvgh4eHh9OvXj08//VR3HC0yMzMZM2YM4eHhDBkyxGXfF4mJiURGRgJw4sQJBg0a\nxJAhQ5g0aVKhft5UBWLLli2kp6cTGxvLmDFjmDJliu5I2qxatYqKFSuyaNEi5syZw2uvvaY7kjaZ\nmZlMnDgRL1nbgvj4eL799ltiY2NZuHChyz6c3blzJ9nZ2cTGxjJq1Kibzr2ysrlz5/Kvf/0rdy7Z\nlClTGD16NB9//DHZ2dls2bKlwGOYqkAkJCQQHBwMQPPmzTl48KDmRPp07dqVZ555BoDs7Gw8PQ3x\nOEmLadOmERERQbVq1XRH0e7LL78kICCAUaNGMXLkSDp06KA7khZ16tQhKysLm81GcnIypUqV0h3J\n6WrXrs2sWbNyf3/o0CFatmwJQNu2bfn6668LPIapPlWunyfh6elJdnY27i44Q6ls2bKAuibPPPMM\nzz33nOZEesTFxVG5cmXatGnDBx98oDuOdn/88QdJSUnMnj2bkydPMnLkSDbYazVCE/H29ubXX3+l\nS5cu/Pnnn8yePVt3JKcLCQnh1KlTub+/+nGzt7d3oeaYmeqT1cfHh9Srpra6anHIcfr0aYYOHUpo\naCjdunXTHUeLuLg4du3aRWRkJEeOHGHcuHGcP39edyxtKlSoQHBwMJ6entStW5cyZcpw4cIF3bGc\nLiYmhuDgYDZu3MiqVasYN24c6enpumNpdfVnZX5zzK75GUcGsrfAwEB27twJwP79+wkICNCcSJ9z\n584xYsQIXnjhBUJDQ3XH0ebjjz9m4cKFLFy4kIYNGzJt2jQqu/BqeUFBQXzxxRcA/Pbbb1y5coWK\nFStqTuV8t9xyCz4+PgD4+vqSmZlJtjP2mTWwxo0bs2fPHgA+//zzQs0nM1UXU0hICLt27SI8PBzA\npR9Sz549m0uXLhEdHc2sWbNwc3Nj7ty5Lj1vxE3WvKB9+/bs3buXsLCw3FF/rnhdhg4dyksvvcTg\nwYNzRzS5+iCGcePG8X//939kZGTg7+9Ply5dCvwZmQchhBAiT6bqYhJCCOE8UiCEEELkSQqEEEKI\nPEmBEEIIkScpEEIIIfIkBUIIIUSepEAIIYTIkxQIIYQQeZICIYQdLFq0iDFjxgDw4osvsnjxYs2J\nhCg5mUkthJ08+eST+Pr6kp6ezvTp03XHEaLEpEAIYSeJiYmEh4cTFxdHo0aNdMcRosSkQAhhB+np\n6URGRhIWFsby5ctZtGiRS2/iJKxBnkEIYQfTp0/ngQceoH///gQHB0sXk7AEaUEIIYTIk7QghBBC\n5EkKhBBCiDxJgRBCCJEnKRBCCCHyJAVCCCFEnqRACCGEyJMUCCGEEHmSAiGEECJP/w8kJwy81tKr\nIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d57438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.title(\"A Sine Curve\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"sin(x)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The position, size, and style of these labels can be adjusted using optional arguments to the function.\n",
    "For more information, see the Matplotlib documentation and the docstrings of each of these functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type.\n",
    "Again, Matplotlib has a built-in way of quickly creating such a legend.\n",
    "It is done via the (you guessed it) ``plt.legend()`` method.\n",
    "Though there are several valid ways of using this, I find it easiest to specify the label of each line using the ``label`` keyword of the plot function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u3r2Kd6g37775LqObjla7HLNzcIBatQyP9XrDKUv7F6+nnqfVLlWbn/r8ROuw\n1mToM+hZq2euHt+kINfpdEyaNMnctWjGgwfQrp1hNIs1TLr0YeMPnwjzCoUrqF1Strz9tmGxEGtw\n+c5lfJb5MKTuEIIbB6tdjsUtXmxY7GXqVLUrybnXS75ORJ8IWi1vRaY+M1cnAlRnhh2Nc3CAo0et\nY/rTh0Y0HIGdjR1eS72IDIqkUpFKapf0XIoCV65A+fKGfwdr+ED9685feId6M7zBcEY0HKF2Obki\nKMg4dYI1qFmiJrsCd9FieQsylUyzrJOaFXljggYNehjiej3s2aNqKWYztP5QRjcZjddSL87fytuz\nHh07BiOsKOsu3r6I11IvghsFvzQhDpAvn/GGoRs3bEi1gpmXq7tWJzIwko8jP2bRr4ty5ZgS5Dl0\n/TosWGA9q8wMrjeYj5t9jHeoN3/e/FPtcp6pTh1Yu1btKszj/K3zeC31YnST0QytP1TtclSzcGFB\n1q9XuwrzqFa8GruDdjNp7yTmR8+3+PHk1EoOlS4NK1aoXYV5DfQYiJ2NHT6hPuwM3MmrxV9VuyTA\n8GG5fbvh+gTAM0a8asqFOxfouaMnnzT7hIEeA9UuR1WjRydSpoz13M9ftVhVdgftxmeZDxn6DIbU\nG2KxY1nBWyHvuHYNlixRuwrz6F+nP1N8puAT6sOpG6fULgeAW7dgwwbruDEL4EzCGbpt7cYkr0kv\nfYjDkx/Me/dCkhVMo1+5aGX2BO1hxsEZzPlljsWOIz1yM9LrrWMR2oeCagdha2NLi2Ut+KnPT9Qs\nUVPVelxdDaexrMGvcb/S7vt2jKk7hv51+qtdTp6zZYth1aGHwxS1rGKRioaeeagPmfpMPmj0gdmP\nIT1yMypXznoWc36o9+u9mdl6Ji2Xt+R4/PFcP/79+zB2LKRY0Yp1B64cwC/Mj3lt5+Hv7q92OXnS\n9OnWEeIPVShcgT199zD36Fxm/DzD7PuXILeQn36CCRPUrsI8AmoGMMdvDq3CWnHwau4ubGpvb5hs\nKV++XD2sxew4v4O3V7/NirdX0Ll6Z7XLyfMUxfA+0tid+09VzqUce/ruYeFvC/k48mMUMy5sKkFu\nIQ0aGGZOtBb+r/kT2imUTqs6se3cNosf7+HvuI0NvPOOdQR5+KlwAjcEsiFgAy0rW8kdTBam04G7\nO7i4qF2JeZQpVIYD/Q6w48IOBv04iEy9eYa7SZBbiIsLvPaa4XFGBqSman/aN78qfmzqsYl+G/sR\ndiLMYscli1/LAAAQO0lEQVTJyABPT4iPt9ghct3i3xYzbNswdvTeQeOyVrRqSS7o3RsK/jMtizXM\nZ+5a0JXIwEgu3L5At/Bu3M/I+R1REuS5YOlSmD3bSe0yzKJhmYZEBkUybtc4Zh2aZZFj2NnBsmVQ\nqpRFdp+rFEXhs72f8dm+z9gdtJvapWqrXZJmZWZC06bW8QHv7ODMlp5bsNHZ0HZF2xwvji5Bngv6\n94cPPkhUuwyzqeFagwP9DzD/1/mM2TkGvWKebtLZs8bHlfL2DAFZkp6ZzsDNA9l4diOHBhyiWvFq\napekaba2sHKldXzAAzjYObCqyyqqFauGd6h3jtYGkCDPBTY2kD+/4fGFC3DkiLr1mEM5l3Ls77ef\n/Vf2ExAeQGp6zu6tzsyEwYMhJsZMBaosKS2JDqs6EJMYw56+eyjlZCXpo7Ly5Y2Pjx0zXkvRKlsb\nW+a1m0enap1otKgRJ/9+9mprzyNBnssuXoQTJ9SuwjyKFyjOrsBd5LPNh1eoF/FJpv/Na2sLu3bB\nK6+YsUCVxNyLofnS5pRxLsOmHptwsreO02p5SWamYT1da1hrQ6fT8UnzTx7dgBdxISLb+5Agz2Ut\nWxpGYTyk9R5Ffrv8hHUOo13VdjRc2JAT17P+KZWRAePGwb1/Tg9awzJgh68dpv7C+vjX8Gd++/nY\n2cg9d5Zga2u4aUiD61A8U6/XexHeLZz/Hfpftl8rQa6izZthtBWsG6DT6ZjQfALTfKfhu8w3y+sX\n2tlB1arWMbQQYOmxpXRY2YH5b81nTNMxml1fU2sUBd5/Hy5dUruSnGtWvhk7eu/I9uuku6CiFi2M\nQxStQY9aPahctDL+P/hz6OohpvhOeWqP9OZNm0c9qX79crlIC0jPTOejiI/Yem4re/vupbprdbVL\neqnodIaJ1KzhtJyppEeuIkdH4+iM1FTrmNe8/iv1iR4YzW/xv9FqeSuuJ11/4vt37kD37sV48ECl\nAs3syt0reIV68efNP/nlnV8kxFXi52dcLu7iReua8ygrJMjziEuXYONGtaswj+IFirOt1zaalmtK\n3QV12fPXnkffK1wYtm27gYODevWZy6azm6i3oB6dqnXix54/UsSxiNolCWDGDNi/X+0qcpecWskj\natSAWY/dX5OSAgUKqFdPTtna2DLZezKNyzam29dTKXvJhoNrGuBg56D5c+Kp6amM3TWWDWc2sKH7\nBhqVbaR2SeIx8+YZL5w/HExg7ZcrpEeeB6WmQt261jEfs18VP6LGfo/zm9uot6Betka15EUHrx6k\ndkht4pPi+fW9XyXE86DHQ3vxYvj8c/VqyS3SI8+DHB3h8GFw0vDw4+3bDXfg1a4N5YoXZ/f4qYQe\nr4bvMl96uvfkv23/i2M+R7XLzLKU9BQm7J7Ait9X8E2bb+hSo4vaJYks6NkTbt9WuwrLkx55HlWo\nkPHxhx/C1q3q1WKK1FSeWEhXp9PRt3Zfjg86zsW7F6n5bU12nM/+MKvcpigK4afCqT63OjGJMZwY\ndEJCXEMcHY1jze/cMUzAZS0rTD1OeuQaMGKEcfY3MJz3y2vn/O7fh7AwGDDAUFvnZ0y17ebsRkiL\nEI4nH2fwlsHULFGTqb5TVV996GlO/n2SEdtHcD35OqGdQvGq4KV2SSIHnJygTx/D/QvWRnrkGlCm\nDBT5Z0DEpUuGoVZ57Y5QW1v480+yPKywTdU2nPrPKbwreOMT6kO/jf24fOeyZYvMorMJZ+mxtgct\nlrWgY7WO/PbebxLiVsDODlq3Nj6fMcNwd6g1kCDXmAoVYM4cY4/81i31xswuX26YHwUMd2dOn26c\nHCwr8tvl54NGH3Du/XO4OblRJ6QOvdf15lj8McsU/AK/XPuFHmt70HRJU14v8Trnh53n/Qbvy232\nVqpjR3jjDePz5GR16khOhq5dc/Y+zlGQR0REEBwcnJNdiGzS6aDaY7OhfvcdfPtt7hxbUQwfHA9V\nrmz4ayGnXPK7MMV3CheHX6RWiVq0+74d3qHehB4LJSnNskN37j24x9JjS2mwsAE91vagnls9zr9/\nnrGeY2WyKyvn7m78/U1LM6wRmphLs03HxRk/OAoWNMz8aWtr+v5MDvIpU6Ywa5ZlFhYQWTduHAwb\nZnw+fLjhFIe5PH4KZ/duGDjQ+Lxx4yc/VHKqcP7CjG46movDLvKfev/hh1M/UGZmGXqt68XK31dy\nM+WmWY5zI/kGK39fSdc1XSk7qyzrz6xnXNNxnHv/HCMbjcQlv5WsKyayzN4eTp8GZ2fD80uX4OOP\nzXuMxy+yjh8PR48an/v65izITf6b8c0336Rly5asXr3a9KMLs3j8wmfnzk/OOTF4MHzxhXEUTEbG\nsy/26PWG+dKrVjU8j48Hb284dcpwDC8vw3NLc7BzoGuNrnSt0ZX4pHg2nNnAypMrGbRlEO7F3Knv\nVp+6bnWp4VqDCoUrUKJgiadOUKVX9Pyd/DeX71zm+PXjHIs/xqFrh7h0+xLNKzSnvXt7FrRfIHdk\nCoAn7jZ2cjKsRvTQkSNw8qRhkRh48YCDmzcNN/WVLWt4/vnnhvfdmDGG54sXm7f2FwZ5eHg4oaGh\nT3xt2rRptGnThiPWsEKClfHyMj7W6w1rXz7sZWRmGgI9MdHw6a/XG+4oPXPG8P2MDHj7bcOE/ba2\nULIkHDhg/IW1UeGKSimnUgyqO4hBdQdxP+M+UbFRHI05SsTFCOYenculO5dISU/BxcEFJ3sn7G3t\neZD5gNT0VG6m3sTFwYVyLuWoVbIWdUrVoffrvannVo98thq/vVRYlKurYVDBQ4ULP7moRUgI/PEH\nfP214fmKFXD+PEycaHi+dauhI/TRR4bnwcHZu36UXTpFMX38w5EjR1i9ejVffvnlU78fHR1N6dKl\nTS7OmiQmJuL8MFFVpNcbA1lR4MIFW6pUMc9K3lll7rZISU8hMT2R5PRk0jPTcbBzwMHWgSIORchv\nZ8F3jxnkld+LvEBLbaEoho7Rw79u79zRkZ6uw9XVPMsexsXF4eHhkeXtLX453s2aZn7PgdjY2DzZ\nFmpM/ZlX20IN0hZGWm4Lc5cdl82lj2T4oRBCaFyOeuT169enfv365qpFCCGECaRHLoQQGidBLoQQ\nGidBLoQQGidBLoQQGidBLoQQGidBLoQQGidBLoQQGidBLoQQGidBLoQQGidBLoQQGidBLoQQGidB\nLoQQGidBLoQQGidBLoQQGidBLoQQGidBLoQQGidBLoQQGidBLoQQGidBLoQQGidBLoQQGidBLoQQ\nGidBLoQQGidBLoQQGidBLoQQGidBLoQQGmdnyouSkpL48MMPSU5OJj09nTFjxlC7dm1z1yaEECIL\nTAryJUuW0LhxYwIDA7l06RLBwcGsW7fO3LUJIYTIApOCvF+/ftjb2wOQkZGBg4ODWYsSQgiRdS8M\n8vDwcEJDQ5/42rRp06hZsyY3btxg1KhRjB8/3mIFCiGEeD6doiiKKS88e/YsH374IaNHj6Zp06ZP\n3SY6OprSpUvnqEBrkZiYiLOzs9pl5AnSFkbSFkbSFkZxcXF4eHhkeXuTTq2cP3+eESNGMHv2bKpV\nq/bcbd3c3Ew5hNWJjY2VtviHtIWRtIWRtIVRXFxctrY3KchnzpxJWloaU6ZMQVEUChUqxNy5c03Z\nlRBCiBwyKcjnzZtn7jqEEEKYSG4IEkIIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMg\nF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0II\njZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMgF0IIjZMg\nF0IIjbMz5UWpqakEBwdz79497O3t+e9//0uJEiXMXZsQQogsMKlHvmbNGmrWrElYWBjt27dnwYIF\n5q5LCCFEFpnUIw8KCkJRFABiY2NxcXExa1FCCCGy7oVBHh4eTmho6BNfmzZtGjVr1iQoKIhz586x\nePFiixUohBDi+XTKw661iS5evMh7771HRETEv74XHR1N6dKlc7J7q5GYmIizs7PaZeQJ0hZG0hZG\n0hZGcXFxeHh4ZHl7k06tzJ8/n5IlS9KxY0cKFCiAra3tM7d1c3Mz5RBWJzY2VtriH9IWRtIWRtIW\nRnFxcdna3qQg79KlC6NHjyY8PBxFUZg2bZopuxFCCGEGJgV5sWLFWLhwoblrEUIIYQK5IUgIITRO\nglwIITROglwIITROglwIITROglwIITROglwIITROglwIITQux7foP090dLSldi2EEFYtO7foWzTI\nhRBCWJ6cWhFCCI2TIBdCCI0ze5ArisLEiRMJCAggMDCQq1evmvsQmpGRkcGoUaPo1asX3bp1IzIy\nUu2SVHfz5k28vLy4dOmS2qWoav78+QQEBNClSxfWrl2rdjmqycjIIDg4mICAAHr37v3S/l4cP36c\nPn36AHDlyhV69uxJ7969mTRpUpZeb/Yg37lzJ2lpaaxatYrg4OCXembETZs2UaRIEVasWMGCBQv4\n7LPP1C5JVRkZGUycOJH8+fOrXYqqjhw5wm+//caqVatYvnx5tqcstSZ79+5Fr9ezatUqhgwZwqxZ\ns9QuKdctXLiQjz/+mPT0dMCwcM/IkSMJCwtDr9ezc+fOF+7D7EEeHR2Np6cnAG+88QYnT5409yE0\no02bNgwfPhwAvV6PnZ1Jk01ajS+++IIePXq89At1HzhwAHd3d4YMGcLgwYPx9vZWuyTVVKhQgczM\nTBRFITExkXz58qldUq4rX748c+fOffT8jz/+oG7dugA0a9aMQ4cOvXAfZk+WpKSkJ1b5sLOzQ6/X\nY2Pz8p2Od3R0BAxtMnz4cD744AOVK1LPunXrKFasGE2aNOG7775TuxxV3b59m9jYWEJCQrh69SqD\nBw9m+/btapelioIFC3Lt2jX8/Py4c+cOISEhapeU61q2bElMTMyj548PJCxYsCCJiYkv3IfZ09XJ\nyYnk5ORHz1/WEH8oLi6OoKAgOnfuTNu2bdUuRzXr1q3j559/pk+fPpw5c4bRo0dz8+ZNtctSReHC\nhfH09MTOzo6KFSvi4ODArVu31C5LFUuXLsXT05MdO3awadMmRo8eTVpamtplqerxvExOTqZQoUIv\nfo25i3jzzTfZu3cvAMeOHcPd3d3ch9CMhIQEBgwYwEcffUTnzp3VLkdVYWFhLF++nOXLl/Pqq6/y\nxRdfUKxYMbXLUoWHhwf79+8H4Pr169y/f58iRYqoXJU6XFxccHJyAsDZ2ZmMjAz0er3KVamrRo0a\nHD16FIB9+/Zl6cYgs59aadmyJT///DMBAQEAL/XFzpCQEO7du8e8efOYO3cuOp2OhQsXYm9vr3Zp\nqtLpdGqXoCovLy+ioqLo2rXro1FeL2ubBAUFMW7cOHr16vVoBMvLfjF89OjRfPLJJ6Snp1O5cmX8\n/Pxe+Bq5s1MIITTu5T15LYQQVkKCXAghNE6CXAghNE6CXAghNE6CXAghNE6CXAghNE6CXAghNE6C\nXAghNO7/ADRl1uEb30mBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d7ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x), '-g', label='sin(x)')\n",
    "plt.plot(x, np.cos(x), ':b', label='cos(x)')\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the ``plt.legend()`` function keeps track of the line style and color, and matches these with the correct label.\n",
    "More information on specifying and formatting plot legends can be found in the ``plt.legend`` docstring; additionally, we will cover some more advanced legend options in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aside: Matplotlib Gotchas\n",
    "\n",
    "While most ``plt`` functions translate directly to ``ax`` methods (such as ``plt.plot()`` → ``ax.plot()``, ``plt.legend()`` → ``ax.legend()``, etc.), this is not the case for all commands.\n",
    "In particular, functions to set limits, labels, and titles are slightly modified.\n",
    "For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:\n",
    "\n",
    "- ``plt.xlabel()``  → ``ax.set_xlabel()``\n",
    "- ``plt.ylabel()`` → ``ax.set_ylabel()``\n",
    "- ``plt.xlim()``  → ``ax.set_xlim()``\n",
    "- ``plt.ylim()`` → ``ax.set_ylim()``\n",
    "- ``plt.title()`` → ``ax.set_title()``\n",
    "\n",
    "In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the ``ax.set()`` method to set all these properties at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i7Y7UVMDBQXYaKo8lSwA/P7EM7OOPa7PPUhWI//znP+bOQTbslVfE3DJJScDg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\nIiJSxQJBRESqWCCIiEgVCwQREan6f8M6gCXBCtQMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107f67cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "ax.plot(x, np.sin(x))\n",
    "ax.set(xlim=(0, 10), ylim=(-2, 2),\n",
    "       xlabel='x', ylabel='sin(x)',\n",
    "       title='A Simple Plot');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.01-Simple-Line-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
